-
The Experience
-
About Stanford GSB
About Our Degree Programs
-
-
The Programs
-
Full-Time Degree Programs
Non-Degree & Certificate Programs
-
-
Faculty & Research
-
Faculty
Faculty Research
Research Hub
Centers & Institutes
-
-
Insights
-
Topics
-
-
Alumni
-
Welcome, Alumni
-
-
Events
-
Admission Events & Information Sessions
-
Evaluating Firm-Level Expected Return Proxies: Implications for Estimating Treatment Effects
Evaluating Firm-Level Expected Return Proxies: Implications for Estimating Treatment Effects
Review of Financial Studies.
2020
We introduce a parsimonious framework for choosing among alternative expected-return proxies (ERPs) when estimating treatment effects. By comparing ERPs’ measurement-error variances in the cross-section and in time series, we provide new evidence on the relative performance of firm-level ERPs nominated by recent studies. Generally, “implied-costs-of-capital” metrics perform best in time series; while “characteristic-based” proxies perform best in the cross-section. Factor-based ERPs, even the latest renditions, perform poorly. We revisit four prior studies that use ex-ante ERPs and illustrate how this framework can potentially alter either the sign or the magnitude of prior inferences.